Training method, evaluation method, electronic device and storage medium

ABSTRACT

The present invention relates to the technical field of field crop cultivation, more particularly to a training method, an evaluation method, an electronic device and a storage medium. According to the present invention, a multispectral three-dimensional point cloud map is obtained through depth information and multispectral information, and the multispectral three-dimensional point cloud map is analyzed by utilizing an FVNet three-dimensional target detection algorithm, thereby acquiring crop feature information. Thus, more comprehensive crop information can be obtained, and a crop state evaluation model constructed based on an artificial neural network can be further trained with the crop feature information.

TECHNICAL FIELD

The present invention relates to the technical field of field cropcultivation, more particularly to a training method, an evaluationmethod, an electronic device and a storage medium.

BACKGROUND

Agriculture is based on plant cultivation, and it is needed tounderstand plants so as to control vital movement of crops, increase theyield and improve the quality. The current states of most crops can beknown by determining surface features of the crops, for example,etiolation and chlorisis of crops suffering from diseases, change ofother color and luster such as purple or red of some crops, blue-greenof leaf color or metal luster of leaf surface (silver leaf) and thelike. Uneven color change on leaf, such as a common floral leaf, isformed by irregular dark and light green colors or yellow and greencolors. The color change part in a shape of irregular patch is a spot,and the cyclic color change part is a ring spot or a concentric spotformed by several ring spots and a stripe color change line grain. Onlythe states of crops are understood can corresponding measures beadopted, so that the crops grow in a given direction. For example, onlynutritional parameters of the crops are understood can fertilization beperformed reasonably; only moisture relation of the crops is understoodcan reasonable irrigation be performed; only the needs of the crops tophotoperiod or vernalization is understood can the crops be illuminatedor shielded manually, and the crop flowering season is controlled; andonly disease characteristics of the crops are understood can pesticidesbe applied with reasonable dosages. As surface characteristics of cropshave important reference value to understand the crop states, eithercultivation and reproduction or new species improvement is quitenecessary to monitor the crops so as to obtain surface characteristicsof the crops.

Existing crops are usually planted in a large scale in a shed, and inthe planting process, a lot of pesticides need to be sprayed. Anexisting crop monitoring method usually uses a common monitoring camerato collect surface characteristics of crops and then inputs thecollected crop information into an artificial neutral network model, soas to judge the growing status and pest and insect status of the crops.As it is difficult to acquire information of all details such as size ofleaf, nutritional elements, lesion location and fruits in the growingprocess of the crops by the common monitoring camera, the cropinformation input into the artificial neutral network model is usuallyincomprehensive. On this basis, the crop states evaluated by theartificial neutral network model are not accurate enough, and theinaccurate evaluation result of the crop states will inevitably affectthe subsequent way of cultivating crops. Improper cultivation mode willmake the crops grow deviating from the given direction or blossom in adelayed manner/in advance, thereby obtaining smaller fruits. If it isslight, the yield is bad, and if it is severe, severe agricultural lossis caused. The existing crop state evaluation method is unable toaccurately evaluate the crop states according to the comprehensive cropinformation, and influence on subsequent cultivation mode has become oneof problems to be urgently solved in the technical field of field cropcultivation. At present, it is an urgent need for a training methodcapable of evaluating the crop states accurately, an evaluation method,an electronic device and a storage medium.

SUMMARY

The present invention aims to overcome at least one defect in the priorart and provide a training method, an evaluation method, an electronicdevice and a storage medium so as to solve the problem that the cropstates are accurately.

The technical solution adopted by the present invention is as follows:

A training method for an artificial neural network model includes:

-   -   acquiring crop monitor information, the crop monitor information        including depth information and multi spectral information;    -   generating a point cloud map according to the depth information        and the multi spectral information;    -   performing point cloud splicing and three-dimensional        reconstruction on the point cloud map to obtain a multispectral        three-dimensional point cloud map;    -   pre-processing the multispectral three-dimensional point cloud        map;    -   analyzing the pre-processed multispectral three-dimensional        point cloud map; based on an FVNet three-dimensional target        detection algorithm to acquire crop feature information;    -   constructing a training set according to the crop feature        information, the training set comprising an original training        set and a target training set, wherein the original training set        is a data set that stores the crop feature information, and the        target training set is a data set that stores marked crop        feature information; and    -   constructing a crop state evaluation model based on the        artificial neural network, and inputting the training set into        the crop state evaluation model for training, so as to output a        crop state evaluation result.

Specifically, the existing crop state evaluation method cannot performevaluation according to comprehensive crop information, so that theevaluation model cannot evaluate accurate crop states. In order toacquire the comprehensive crop information and improve the biologicalstate accuracy evaluated, the solution adopts a training method, anevaluation method, an electronic device and a storage medium. First,horizontal and perpendicular scanning imaging is performed respectivelyin a target region by utilizing a visual unit and a spectral unit so asto respectively obtain depth information and multispectral informationof the target region. The visual unit is a stereoscopic vision scanningsystem which is usually a high definition stereoscopic camera. Thespectral unit is a multispectral collecting system which is usually amultispectral camera. Then, a point cloud map is generated according tothe depth information and the multispectral information, point cloudsplicing and three-dimensional reconstruction are performed on the pointcloud map to obtain a multispectral three-dimensional point cloud map.Finally, the multispectral three-dimensional point cloud map ispre-processed to eliminate abnormal values, and the pre-processedmultispectral three-dimensional point cloud map is analyzed based on anFVNet three-dimensional target detection algorithm to acquire cropfeature information. The crop feature information is marked, and thetraining set is established according to the marked crop featureinformation. The training set is input into an artificial neutralnetwork to train, and the current state of the crop corresponding to thecrop information is evaluated to output a crop state evaluation result.The FVNet three-dimensional target detection algorithm realizesreal-time performance at a speed of 12 milliseconds per cloud pointsample. Compared with an existing algorithm that detects images or pointcloud of the camera, it has advantages in precision and speed. Accordingto the solution, the multispectral three-dimensional point cloud map isobtained through depth information and multispectral information, andthe multispectral three-dimensional point cloud map is analyzed byutilizing an FVNet three-dimensional target detection algorithm, therebyacquiring crop feature information. Thus, more comprehensive cropinformation can be obtained, and a crop state evaluation modelconstructed based on an artificial neural network can be further trainedwith the crop feature information. The crop state is evaluatedaccurately through the trained crop state evaluation model, so thateffective information is provided for subsequent crop cultivation.

Further, the crop feature information includes a lesion location, a leafarea index, a fruit color distribution, a fruit volume and a nutritionalparameter.

Specifically, the lesion location, the leaf area index, the fruit colordistribution and the fruit volume acquired by utilizing themultispectral information can be used for training the artificialneutral network model. The artificial neutral network model can positionthe lesion location accurately according to color so as to identify thetype of plant diseases and insect pests and extend of harm. The accuratefocus information can improve the efficiency of eliminating insect pestsand prevent insect pests from damaging the crops. The growing conditionof the crops can be understood through the leaf area index (LAI), andthe cultivation mode of the crops is adjusted timely. The growth vigorof the crops can be tracked and analyzed in real time through the fruitcolor distribution and the fruit volume, so that intelligent refinedmanagement and planting of the crop growth process is realized. Inaddition, the nutritional parameters contained in the crops can beacquired according to analysis of the fruit color and the LAI, andcorresponding cultivation operation can be adopted according to thenutritional parameters, so that it is guaranteed that the crops grow inexpected directions.

Further, after the multispectral three-dimensional point cloud map ispre-processed, and before the pre-processed multispectralthree-dimensional point cloud map is analyzed based on an FVNetthree-dimensional target detection algorithm to acquire crop featureinformation, the method further includes:

-   -   optimizing high-spectral wave bands in the multispectral        three-dimensional point cloud map based on a DFLDE algorithm so        as to acquire effective wave bands.

Specifically, DFLDE (Dynamic Fitness Landscape Differential Evolution)is a differential evolution algorithm based on dynamic fitnesslandscape, and the differential evolution algorithm is a multi-target(continuous variable) optimization algorithm (MOEAs) used for solvingthe global optimal solution in a multidimensional space. Compared withgenetic algorithm, the differential evolution algorithm has the samepoint that an initial population is generated randomly. By taking afitness value of each individual in the population as a selectioncriterion, a main process also includes three steps: variation,intersection and selection. The difference is that the genetic algorithmcontrols parent hybridization according to the fitness value and aprobability value that filial generation generated is selected aftervariation, and the probability that the individual with large fitness inthe maximization problem is selected is great correspondingly. Avariation vector of the differential evolution algorithm is generated bya differential vector of a parent generation and is intersected with thedifferential vector of the parent generation to generate a newindividual vector, and is directly selected with its parent generationindividual. Obviously, the approximation effect of the differentialevolution algorithm relative to the genetic algorithm is moreremarkable. High-spectral waveforms of the point cloud map are optimizedby using a DFLDE so as to screen effective wave bands therein, so thatthe accuracy of information extraction can be further improved.

Further, pre-processing the multispectral three-dimensional point cloudmap includes:

-   -   smoothing, correcting, deriving, normalizing and dimensionality        reducing.

Further, it includes:

-   -   collecting crop monitor information in a target region by a        collecting device,    -   the target region being a region where a crop is located; and    -   inputting the crop information into the trained artificial        neural network model to perform crop state evaluation, the        artificial neural network model being trained by the training        method.

Further, the crop is a strawberry.

Specifically, in the solution, strawberry is selected as the crop.First, a strawberry image carrying the multispectral information iscollected and marked. By taking images of fruits with differentmaturities of batch of strawberries as training samples, the artificialneutral network is trained to obtain a color average classificationmodel of the artificial neutral network for detecting strawberry fruits.Then, an RGB image and a depth image of the target region are acquired.Finally, values R, G and B of the RGB image are taken as input of thecolor average classification model of the artificial neutral network.Finally, pixel points of the RGB image are classified by virtue of thecolor average classification model of the artificial neutral network toremove background pixels, so as to obtain an image of a strawberry fruitpixel region. The image of the strawberry fruit pixel region isanalyzed. Whether the strawberry is ripe is judged according to a pixeltype ratio. If the ratio of the pixel type representing maturity in theimage reaches a threshold value, the strawberry is judged ripe and it isno ripe on the contrary. To identify whether the strawberry is ripemakes a user acquire timely so as to collect the strawberry, therebyavoiding overripe of fruits.

Further, the collecting device includes:

-   -   a visual unit, configured to collect depth information of the        target region; and    -   a spectral unit, configured to collect spectral information of        the target region.

Specifically,

-   -   further, the collecting device further includes:    -   a navigation unit, configured to plan a path to collect the        depth information and the spectral information;    -   a moving unit, configured to transport other units according to        the planned path;    -   an adjusting unit, configured to adjust collecting angles of the        visual unit and the spectral unit;    -   a communication unit, configured to transmit the depth        information and the spectral information to a cloud server; and    -   a display unit, configured to display information of the crop        state evaluation for a user to look up.

Further, the collecting device further includes a power supply unit anda master control unit. The above-mentioned all units can be assembled towalk in a shed where strawberries are planted so as to monitor thegrowth vigor of the strawberries. The navigation unit is configured toplan a moving path of the moving unit and navigate the moving unit. Themoving unit usually adopts bionic mechanical legs that can walk freelyto avoid obstacles emergently. The communication unit, the power supplyunit and the master control unit are usually integrated to the movingunit. The adjusting unit is mounted on the top of the moving unit, andusually adopts a freely stretching guide rail. The guide rail can eitherstretch upwards or move left and right. The visual unit and the spectralunit are arranged on the adjusting unit, namely, the stereoscopic cameraand the three-dimensional scanning imaging system fixed in position areembedded into the guide rail. The moving unit can be autonomouslynavigated through the navigation unit to walk among strawberry plantingrows. The adjusting unit can adjust the positions of the visual unit andthe spectral unit, so that the visual unit and the spectral unit obtaingood photographing angles. The visual unit and the spectral unit collectthe depth information and the multi spectral information of the crops inreal time. After collection, the communication unit transmits the depthinformation and the spectral information to a cloud serve in wirelesscommunication modes such as 5G/4G. The cloud server performs analysisthrough the artificial neutral network model, a result is thentransmitted to a display unit, and the display unit displays theanalysis result on a display for a user to look up.

An electronic device includes:

-   -   a processor and a memory,    -   the memory having a computer readable instruction stored        thereon, the computer readable instruction implementing the        training method for an artificial neural network model according        to any one above or the crop state evaluation method based on an        artificial neural network model according to any one above when        being executed by the processor.

A computer readable storage medium, the computer readable storage mediumhaving a processing program stored thereon, wherein the processingprogram is executable by one or more processors to implement thetraining method for an artificial neural network model according to anyone above or the crop state evaluation method based on an artificialneural network model according to any one above.

Compared with the prior art, the present invention has the beneficialeffects: the multispectral three-dimensional point cloud map is obtainedthrough depth information and multispectral information, and themultispectral three-dimensional point cloud map is analyzed by utilizingan FVNet three-dimensional target detection algorithm, thereby acquiringcrop feature information. Thus, more comprehensive crop information canbe obtained, and a crop state evaluation model constructed based on anartificial neural network can be further trained with the crop featureinformation. The crop state is evaluated accurately through the trainedcrop state evaluation model, so that effective information is providedfor subsequent crop cultivation. In addition, high-spectral waveforms ofthe point cloud map are optimized by using a DFLDE so as to screeneffective wave bands therein, so that the accuracy of informationextraction can be further improved.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram of an evaluation method of the presentinvention.

DETAILED DESCRIPTION OF EMBODIMENTS

The accompanying drawings of the present invention are merely used forexemplary description and are not construed as limitation to the presentinvention. In order to better describe the embodiments, some parts inthe drawings will be omitted, amplified or lessened and the drawings donot represent the dimensions of actual products. Those skilled in theart can understand that some known structures and description thereof inthe drawings may be omitted.

Embodiment 1

The embodiment provides a training method for an artificial neuralnetwork model, including:

-   -   crop monitor information is acquired, the crop monitor        information including depth information and multispectral        information;    -   a point cloud map is generated according to the depth        information and the multispectral information;    -   point cloud splicing and three-dimensional reconstruction are        performed on the point cloud map to obtain a multispectral        three-dimensional point cloud map;    -   the multispectral three-dimensional point cloud map is        pre-processed;    -   the pre-processed multispectral three-dimensional point cloud        map is analyzed based on an FVNet three-dimensional target        detection algorithm to acquire crop feature information;    -   a training set is constructed according to the crop feature        information, the training set including an original training set        and a target training set, where the original training set is a        data set that stores the crop feature information, and the        target training set is a data set that stores marked crop        feature information; and    -   a crop state evaluation model is constructed based on the        artificial neural network, and the training set is inputted into        the crop state evaluation model for training, so as to output a        crop state evaluation result.

Specifically, the existing crop state evaluation method cannot performevaluation according to comprehensive crop information, so that theevaluation model cannot evaluate accurate crop states. In order toacquire the comprehensive crop information and improve the biologicalstate accuracy evaluated, the solution adopts a training method, anevaluation method, an electronic device and a storage medium. First,horizontal and perpendicular scanning imaging is performed respectivelyin a target region by utilizing a visual unit and a spectral unit so asto respectively obtain depth information and multispectral informationof the target region. The visual unit is a stereoscopic vision scanningsystem which is usually a high definition stereoscopic camera. Thespectral unit is a multispectral collecting system which is usually amultispectral camera. Then, a point cloud map is generated according tothe depth information and the multispectral information, point cloudsplicing and three-dimensional reconstruction are performed on the pointcloud map to obtain a multispectral three-dimensional point cloud map.Finally, the multispectral three-dimensional point cloud map ispre-processed to eliminate abnormal values, and the pre-processedmultispectral three-dimensional point cloud map is analyzed based on anFVNet three-dimensional target detection algorithm to acquire cropfeature information. The crop feature information is marked, and thetraining set is established according to the marked crop featureinformation. The training set is input into an artificial neutralnetwork to train, and the current state of the crop corresponding to thecrop information is evaluated to output a crop state evaluation result.The FVNet three-dimensional target detection algorithm realizesreal-time performance at a speed of 12 milliseconds per cloud pointsample. Compared with an existing algorithm that detects images or pointcloud of the camera, it has advantages in precision and speed. Accordingto the solution, the multispectral three-dimensional point cloud map isobtained through depth information and multispectral information, andthe multispectral three-dimensional point cloud map is analyzed byutilizing an FVNet three-dimensional target detection algorithm, therebyacquiring crop feature information. Thus, more comprehensive cropinformation can be obtained, and a crop state evaluation modelconstructed based on an artificial neural network can be further trainedwith the crop feature information. The crop state is evaluatedaccurately through the trained crop state evaluation model, so thateffective information is provided for subsequent crop cultivation.

Further, the crop feature information includes a lesion location, a leafarea index, a fruit color distribution, a fruit volume and a nutritionalparameter.

Specifically, the lesion location, the leaf area index, the fruit colordistribution and the fruit volume acquired by utilizing themultispectral information can be used for training the artificialneutral network model. The artificial neutral network model can positionthe lesion location accurately according to color so as to identify thetype of plant diseases and insect pests and extend of harm. The accuratefocus information can improve the efficiency of eliminating insect pestsand prevent insect pests from damaging the crops. The growing conditionof the crops can be understood through the leaf area index (LAI), andthe cultivation mode of the crops is adjusted timely. The growth vigorof the crops can be tracked and analyzed in real time through the fruitcolor distribution and the fruit volume, so that intelligent refinedmanagement and planting of the crop growth process is realized. Inaddition, the nutritional parameters contained in the crops can beacquired according to analysis of the fruit color and the LAI, andcorresponding cultivation operation can be adopted according to thenutritional parameters, so that it is guaranteed that the crops grow inexpected directions.

Further, after the multispectral three-dimensional point cloud map ispre-processed, and before the pre-processed multispectralthree-dimensional point cloud map is analyzed based on an FVNetthree-dimensional target detection algorithm to acquire crop featureinformation, the method further includes:

-   -   high-spectral wave bands in the multispectral three-dimensional        point cloud map are optimized based on a DFLDE algorithm so as        to acquire effective wave bands.

Specifically, DFLDE (Dynamic Fitness Landscape Differential Evolution)is a differential evolution algorithm based on dynamic fitnesslandscape, and the differential evolution algorithm is a multi-target(continuous variable) optimization algorithm (MOEAs) for solving theglobal optimal solution in the multi-dimensional space. Compared withgenetic algorithm, the differential evolution algorithm has the samepoint that an initial population is generated randomly. By taking afitness value of each individual in the population as a selectioncriterion, a main process also includes three steps: variation,intersection and selection. The difference is that the genetic algorithmcontrols parent hybridization according to the fitness value and aprobability value that filial generation generated is selected aftervariation, and the probability that the individual with large fitness inthe maximization problem is selected is great correspondingly. Avariation vector of the differential evolution algorithm is generated bya differential vector of a parent generation and is intersected with thedifferential vector of the parent generation to generate a newindividual vector, and is directly selected with its parent generationindividual. Obviously, the approximation effect of the differentialevolution algorithm relative to the genetic algorithm is moreremarkable. High-spectral waveforms of the point cloud map are optimizedby using a DFLDE so as to screen effective wave bands therein, so thatthe accuracy of information extraction can be further improved.

Further, pre-processing the multispectral three-dimensional point cloudmap includes:

-   -   smoothing, correcting, deriving, normalizing and dimensionality        reducing.

FIG. 1 is a flow diagram of an evaluation method of the presentinvention, as shown in FIG., including:

-   -   the crop monitor information in a target region is collected by        a collecting device,    -   the target region being a region where the crop is located;    -   the crop information is inputted into the trained artificial        neural network model to perform crop state evaluation, the        artificial neural network model being trained by the training        method.

Further, the crop is a strawberry.

Specifically, in the solution, strawberry is selected as the crop.First, a strawberry image carrying the multispectral information iscollected and marked. By taking images of fruits with differentmaturities of batch of strawberries as training samples, the artificialneutral network is trained to obtain a color average classificationmodel of the artificial neutral network for detecting strawberry fruits.Then, an RGB image and a depth image of the target region are acquired.Finally, values R, G and B of the RGB image are taken as input of thecolor average classification model of the artificial neutral network.Finally, pixel points of the RGB image are classified by virtue of thecolor average classification model of the artificial neutral network toremove background pixels, so as to obtain an image of a strawberry fruitpixel region. The image of the strawberry fruit pixel region isanalyzed. Whether the strawberry is ripe is judged according to a pixeltype ratio. If the ratio of the pixel type representing maturity in theimage reaches a threshold value, the strawberry is judged ripe and it isno ripe on the contrary. To identify whether the strawberry is ripemakes a user acquire timely so as to collect the strawberry, therebyavoiding overripe of fruits.

Further, the collecting device includes:

-   -   a visual unit, configured to collect depth information of the        target region; and    -   a spectral unit, configured to collect spectral information of        the target region.

Specifically,

-   -   further, the collecting device further includes:    -   a navigation unit, configured to plan a path to collect the        depth information and the spectral information;    -   a moving unit, configured to transport other units according to        the planned path;    -   an adjusting unit, configured to adjust collecting angles of the        visual unit and the spectral unit;    -   a communication unit, configured to transmit the depth        information and the spectral information to a cloud server; and    -   a display unit, configured to display information of the crop        state evaluation for a user to look up.

Further, the collecting device further includes a power supply unit anda master control unit. The above-mentioned all units can be assembled towalk in a shed where strawberries are planted so as to monitor thegrowth vigor of the strawberries. The navigation unit is configured toplan a moving path of the moving unit and navigate the moving unit. Themoving unit usually adopts bionic mechanical legs that can walk freelyto avoid obstacles emergently. The communication unit, the power supplyunit and the master control unit are usually integrated to the movingunit. The adjusting unit is mounted on the top of the moving unit, andusually adopts a freely stretching guide rail. The guide rail can eitherstretch upwards or move left and right. The visual unit and the spectralunit are arranged on the adjusting unit, namely, the stereoscopic cameraand the three-dimensional scanning imaging system fixed in position areembedded into the guide rail. The moving unit can be autonomouslynavigated through the navigation unit to walk among strawberry plantingrows. The adjusting unit can adjust the positions of the visual unit andthe spectral unit, so that the visual unit and the spectral unit obtaingood photographing angles. The visual unit and the spectral unit collectthe depth information and the multi spectral information of the crops inreal time. After collection, the communication unit transmits the depthinformation and the spectral information to a cloud serve in wirelesscommunication modes such as 5G/4G. The cloud server performs analysisthrough the artificial neutral network model, a result is thentransmitted to a display unit, and the display unit displays theanalysis result on a display for a user to look up.

An electronic device includes:

-   -   a processor and a memory,    -   the memory having a computer readable instruction stored        thereon, the computer readable instruction implementing the        training method for an artificial neural network model according        to any one above or the crop state evaluation method based on an        artificial neural network model according to any one above when        being executed by the processor.

A computer readable storage medium, the computer readable storage mediumhaving a processing program stored thereon, wherein the processingprogram is executable by one or more processors to implement thetraining method for an artificial neural network model according to anyone above or the crop state evaluation method based on an artificialneural network model according to any one above.

Obviously, the embodiments of the present invention are merely examplesmade for describing the present invention clearly and are not to limitthe specific embodiments of the present invention. Any modification,equivalent replacement, improvement, etc. made within the spirit andprinciple of the claims of the present invention shall be regarded aswithin the protection scope of the claims of the present invention.

The invention claimed is:
 1. A training method for an artificial neuralnetwork model, comprising: acquiring crop monitor information, the cropmonitor information comprising depth information and multispectralinformation; generating a point cloud map according to the depthinformation and the multispectral information; performing point cloudsplicing and three-dimensional reconstruction on the point cloud map toobtain a multispectral three-dimensional point cloud map; pre-processingthe multispectral three-dimensional point cloud map; optimizinghigh-spectral wave bands in the multispectral three-dimensional pointcloud map based on a Dynamic Fitness Landscape Differential Evolution(DFLDE) algorithm so as to acquire effective wave bands; analyzing thepre-processed multispectral three-dimensional point cloud map based on athree-dimensional target detection algorithm to acquire crop featureinformation; constructing a training set according to the crop featureinformation, the training set comprising an original training set and atarget training set, wherein the original training set is a data setthat stores the crop feature information, and the target training set isa data set that stores marked crop feature information; and constructinga crop state evaluation model based on the artificial neural network,and inputting the training set into the crop state evaluation model fortraining, so as to output a crop state evaluation result.
 2. Thetraining method for an artificial neural network model according toclaim 1, wherein the crop feature information comprises a lesionlocation, a leaf area index, a fruit color distribution, a fruit volumeand a nutritional parameter.
 3. The training method for an artificialneural network model according to claim 1, wherein the pre-processingthe multispectral three-dimensional point cloud map comprises:smoothing, correcting, deriving, normalizing and dimensionalityreducing.
 4. A crop state evaluation method based on an artificialneural network model, comprising: collecting crop monitor information ina target region by a collecting device, the target region being a regionwhere a crop is located; and inputting the crop information into atrained artificial neural network model to perform crop stateevaluation, the artificial neural network model being trained by atraining method including: acquiring crop monitor information, the cropmonitor information comprising depth information and multispectralinformation; generating a point cloud map according to the depthinformation and the multispectral information; performing point cloudsplicing and three-dimensional reconstruction on the point cloud map toobtain a multispectral three-dimensional point cloud map; pre-processingthe multispectral three-dimensional point cloud map; optimizinghigh-spectral wave bands in the multispectral three-dimensional pointcloud map based on a Dynamic Fitness Landscape Differential Evolution(DFLDE) algorithm so as to acquire effective wave bands; analyzing thepre-processed multispectral three-dimensional point cloud map based on athree-dimensional target detection algorithm to acquire crop featureinformation; constructing a training set according to the crop featureinformation, the training set comprising an original training set and atarget training set, wherein the original training set is a data setthat stores the crop feature information, and the target training set isa data set that stores marked crop feature information; and constructinga crop state evaluation model based on the artificial neural network,and inputting the training set into the crop state evaluation model fortraining, so as to output a crop state evaluation result.
 5. The cropstate evaluation method based on an artificial neural network modelaccording to claim 4, wherein the crop is a strawberry.
 6. The cropstate evaluation method based on an artificial neural network modelaccording to claim 4, wherein the collecting device comprises: a visualunit, configured to collect depth information of the target region; anda spectral unit, configured to collect spectral information of thetarget region.
 7. The crop state evaluation method based on anartificial neural network model according to claim 6, wherein thecollecting device further comprises: a navigation unit, configured toplan a path to collect the depth information and the spectralinformation; a moving unit, configured to transport other unitsaccording to the planned path; an adjusting unit, configured to adjustcollecting angles of the visual unit and the spectral unit; acommunication unit, configured to transmit the depth information and thespectral information to a cloud server; and a display unit, configuredto display information of the crop state evaluation for a user to lookup.